Achraf Bahamou

Quantitative Researcher

I completed my PhD in Operations Research at Columbia University in 2023. 
I was fortunate to be advised by Prof. Donald Goldfarb and Prof. Omar Besbes.

My research interests include:
- Optimization algorithms for Machine learning and Deep learning.
- Value of information and mechanism design in low/finite informational environments.

My PhD dissertation was "Topics in Deep Learning and Data-driven Optimization".

Prior to Columbia, I graduated from
Ecole Polytechnique, Paris with BS and MS degrees in Applied Mathematics and Statistics. 




achraf.bahamou [at] columbia [dot] edu

Google Scholar

Industry Experience


Quantitative Research Intern, Jump Trading 

Chicago, IL

- Research, feature engineering, model selection and writing execution algorithms.


Applied Scientist Intern, Amazon

Seattle, WA

- Research on Data-Driven Pricing using Machine Learning.


Data Scientist Intern, Google 

New York, NY

- Worked with Active-Learning-For-All (ALFA) and Neurosurgeon teams, Google Research NYC.
- Research on active learning gains forecasting.


Quantitative Research Intern, Hellebore Capital Limited 

London, UK

- Worked on modeling irregularly spaced trades arrival times using multivariate Hawkes processes.


Data Analyst Intern, Air France-KLM 

Paris, FR

- Worked on estimating the unconstrained demand on long-haul flights using time series forecasting models and machine learning.


My research in Optimization involves developing efficient 1st and 2nd order optimization algorithms for minimizing loss functions in Machine Learning and Deep Learning frameworks:

- Practical BFGS Methods for Training Deep Neural Networks. joint with Prof. Goldfarb and Yi Ren 

Accepted in Neurips 2020, Spotlight presentation [Neurips Proceedings] 

- A Mini-Block Fisher Method for Deep Neural Networks. joint with Prof. Goldfarb and Yi Ren

Accepted in AISTATS 2023 [AISTATS Proceedings] 

- Stochastic Flows and Geometric Optimization on the Orthogonal Group. joint with Krzysztof Choromanski, Google Brain NY et al. Accepted in ICML 2020 [ICML Proceedings]

- Kronecker-factored Quasi-Newton Methods for Deep Learning. joint with Prof. Goldfarb and Yi Ren

 Preprint [arXiv] 

My research in data-driven decision-making focuses on characterizing the value of information and optimal pricing mechanisms in low/finite informational environments:

- Optimal Pricing with a Single Point. joint with Prof.Omar Besbes and Amine Allouah

Journal of Management Science, Accepted in EC 2021 [SSRN] [MS Article]

Presented in RMP 2021 (spotlight session), MSOM 2021, EC 2021, Informs 2020

- Pricing with Samples. joint with Prof.Omar Besbes and Amine Allouah. 

Journal of Operations Research, Accepted in EC 2021. [SSRN] [OR Article]

- Fast revenue maximization with few experiments. joint with Prof.Omar Besbes and Omar Mouchtaki

(Ongoing work).

Previous research:

- Hawkes processes for credit indices time series analysis: How random are trades arrival times? 

joint with Maud Doumergue, Philippe Donnat, Hellebore Capital LLC. ITISE 2018 International Conference on Time Series and Forecasting accepted paper[] [ITISE 2018 Proceedings]



Columbia University in the City of New York


Ph.D. in Operations Research. 
M.S in Operations Research. 
- Advisors: Prof. Donald Goldfarb and Prof. Omar Besbes.
- Thesis: Topics in Deep Learning and Data-driven Optimization.
- Deming Center Doctoral Fellow.


Ecole Polytechnique, Paris


B.S and M.S in Applied Mathematics.
- Relevant Courses: Machine learning, Statistics, Times Series Analysis, Stochastic processes and Monte-Carlo methods, Optimization, Random modeling, Analysis of Algorithms, Statistical Physics, Quantum Mechanics.


- Deming Center Doctoral Fellowship. Deming Center Doctoral Fellow 2021.

- Meta PhD Research Fellowship, 2022 Finalist.

- Postgraduate Excellence Scholarship. OCP Foundation.

- French Government Major-Excellence Scholarship. The French Ministry for Foreign Affairs.

- Hassan II Academy of Science and Technology Fellowship. Ranked First in The National Open Competition Of Science and Technology - Physics category.


- Optimization Models and Methods, M.S OR core course (IEORE4004), Columbia University

Head Teaching Assistant (Spring 2021), Around 170 students

- Columbia Center for Teaching and Learning, Research Assistant (Fall 2019, Spring 2020)

Collaborating with the Center for Teaching and Learning to introduce an auto-grading framework and incorporate digital technology into the teaching environment to facilitate active learning.  

- Business Analytics, (IEOR 4650), Columbia University

Head Teaching Assistant (Fall 2019) , Around 70 students [Info]  

- Simulation Modeling and Analysis, (IEORE3404), Columbia University

Head Teaching Assistant (Spring 2019, 2020), Around 90 students [Info]  

- Simulation, M.S OR core course (IEORE4404), Columbia University

Head Teaching Assistant (Fall 2018, 2020), Around 130 students [Info] 


Email: achraf.bahamou [at] columbia [dot] edu

© Achraf Bahamou 2023

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